Notes:

Java required for OpenStreetMap, which doesnt require Google API to use maps for spatial plotting, unlike ggmap.

Questions: levels the data is observed at years the data is collected normalization explained. What is it normalized to spacial domain

External Data Sets ideas:

  1. Child care accessibility
  2. Lisa’s education data set
  3. BLS career opportunities within the GEOIDs

Data input

The data behind the Urban Institute’s Unequal Commute feature, published on October 6, 2020.

Takeaways from the report:

  1. Access to jobs varies by neighborhood
  2. A lack of public transit access hinders transit dependent workers’ ability to commute
  3. People of color have less access to safe and affordable transportation
  4. Better data are needed to help increase transportation equity and access to opportunity

It is restricted to only the access to job opportunity in the Lansing, Michigan; Seattle, Washington; Baltimore, Maryland; and Nashville, Tennessee MSAs. Each census block represented by a GEOID is a subdivision of a census tract that generally contains between 600 and 3,000 people.

Job access is calculcated based on a gravity model of the combined auto and transit access for low wage workers in a block group. This is defined by the number of jobs in an area multiplied by the impidence function of travel time divided by the sum of the total population of commuters and the impidence function of travel time for all of them.

file_js_path <- "C:/Users/prym311/Documents/JSM Data Science Expo STATS/job_access_gap_ANA/job_access_gap.json"
job_access_csv <- "C:/Users/prym311/Documents/JSM Data Science Expo STATS/job_access_gap_ANA/job_access_gap.csv"
job_access_gap_dict <- "C:/Users/prym311/Documents/JSM Data Science Expo STATS/job_access_gap_ANA/job_access_gap_dict.csv"
job_access_equity <- "C:/Users/prym311/Documents/JSM Data Science Expo STATS/job_access_gap_ANA/job_access_gap_equity.csv"
job_access_gap_equity_dict <- "C:/Users/prym311/Documents/JSM Data Science Expo STATS/job_access_gap_ANA/job_access_gap_equity_dict.csv"


file_js = FROM_GeoJson(url_file_string = file_js_path)
job_access <- read.csv(job_access_csv)
job_access_gap <- read.csv(job_access_equity)
definitions <- read.csv(job_access_gap_dict)
definitions
##             col_name
## 1              GEOID
## 2   OverallJobAccess
## 3  lowwagelaborforce
## 4    Access30Transit
## 5 Access30Transit_ts
## 6                MSA
## 7    spatialmismatch
## 8           geometry
##                                                                                        definition
## 1                                                                       FIPS code for block group
## 2                           Normalized measure of overall access to jobs for low wage job seekers
## 3                                    Count of low wage labor force in block group from LODES data
## 4        Normalized measure of jobs accessible within 30 minutes via public transit at peak hours
## 5 Normalized measure of jobs accessible within 30 minutes via public transit at third shift hours
## 6                 Shortened name of metropolitan area (Baltimore, Nashville, Lansing, or Seattle)
## 7                    Normalized measure of spatial mismatch between low wage job seekers and jobs
## 8                                              Polygon geometry representing block group boundary

Leading Questions: How is the overall job access related to the block group workers commute from? Is overall job access more correlated with day time versus night time workers? How has COVID impacted the block regions and job access in these four metropolitan areas. Do people of color have equal access to job opportunities?

cntrd <- function(x) {
  as.vector(centroid(as.matrix(x[,c("long", "lat")])))
}
## Regions defined for each Polygons

Time of Day Comparison

Explore Data

## Using type, cbsa_name as id variables